Analytics, Monitoring & Tooling

• Build and maintain dashboards, monitoring systems, and automated reporting to track product, business, and model performance.

• Develop scalable analytics pipelines to surface key metrics, detect anomalies, and support timely issue diagnosis..

• Define and refine KPIs, using structured, hypothesis-driven analysis to understand performance changes and long-term trends.

Insights Discovery & Model Evaluation

• Analyze large datasets to identify trends, diagnose performance changes, and uncover growth opportunities.

• Conduct exploratory analysis, root-cause investigations, and hypothesis-driven deep dives.

• Support engineering and research science teams in evaluating model performance, including stability, calibration, and long-term health.

Experimentation & Measurement

• Assist in designing A/B tests, computing key metrics, and interpreting results.

• Apply basic statistical reasoning (e.g., variance, confidence intervals, significance testing) to support decision-making.

Cross-Functional Collaboration

• Partner with engineering to integrate new data sources, refine data structures, and enable scalable analytics.

• Work with product and business teams to understand analytical needs and translate them into actionable solutions.

Minimum Qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, or another quantitative discipline.

• Basic understanding of core statistical concepts and introductory modeling techniques, with the ability to apply them in practical analysis

• 0–3 years of experience in data analytics or data science (internships or projects count).
  Proficiency with SQL and at least one analytical programming language (Python preferred).

• Ability to work with large datasets and translate findings into actionable recommendations.

• Ability to translate business questions into analytical frameworks and communicate insights effectively to both technical and non-technical audiences.

• Curious, proactive mindset with a desire to learn quickly and contribute meaningfully.

Preferred Qualifications

• Master’s degree in Data Science or a related quantitative discipline.

• Experience with BI tools such as Looker, Tableau, Superset, Metabase, or similar.

• Familiarity with cloud data warehouses (e.g., BigQuery, Snowflake) and workflow orchestration tools such as Airflow.

• Exposure to A/B testing, experiment design, or statistical evaluation.

• Understanding of digital advertising, performance marketing metrics, or online marketplace dynamics.

• Experience collaborating with cross-functional teams (engineering, product, business).

What You’ll Gain

• Hands-on experience with real production analytics, monitoring systems, and model evaluation.

• Opportunities to build foundational infrastructure that scales across teams.

• Exposure to high-scale data challenges and real-world ML performance considerations.

• A meaningful role in driving insights that inform product and business strategy.